GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models

MCML Authors

Xiaoxiang Zhu

Prof. Dr.

Principal Investigator

Data Science in Earth Observation

Sining Chen

→ Group Xiaoxiang Zhu
 Data Science in Earth Observation

Abstract

We introduce GlobalBuildingAtlas, a publicly available dataset providing global and complete coverage of building polygons, heights and Level of Detail 1 (LoD1) 3D building models. This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D form at the individual building level on a global scale. Towards this dataset, we developed machine learning-based pipelines to derive building polygons and heights (called this http URL) from global PlanetScope satellite data, respectively. Also a quality-based fusion strategy was employed to generate higher-quality polygons (called this http URL) based on existing open building polygons, including our own derived one. With more than 2.75 billion buildings worldwide, this http URL surpasses the most comprehensive database to date by more than 1 billion buildings. this http URL offers the most detailed and accurate global 3D building height maps to date, achieving a spatial resolution of 3x3 meters-30 times finer than previous global products (90 m), enabling a high-resolution and reliable analysis of building volumes at both local and global scales. Finally, we generated a global LoD1 building model (called GBA.LoD1) from the resulting this http URL and this http URL. GBA.LoD1 represents the first complete global LoD1 building models, including 2.68 billion building instances with predicted heights, i.e., with a height completeness of more than 97%, achieving RMSEs ranging from 1.5 m to 8.9 m across different continents. With its height accuracy, comprehensive global coverage and rich spatial details, GlobalBuildingAltas offers novel insights on the status quo of global buildings, which unlocks unprecedented geospatial analysis possibilities, as showcased by a better illustration of where people live and a more comprehensive monitoring of the progress on the 11th Sustainable Development Goal of the United Nations.

misc

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Preprint

Jun. 2025

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Authors

X. Zhu • S. Chen • F. Zhang • Y. Shi • Y. Wang

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Links

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Research Area

 C3 | Physics and Geo Sciences